Booz Allen's Decision Analytics Center of Excellence employs modeling and simulation, decision science, operations research, and quantitative analysis to deliver data-driven solutions that improve performance, optimize flows, and help stakeholders understand what and when events are likely to occur.
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Airline Analytics: Decision Analytics Centers of Excellence
1. Ready for what’s next.
Airline Analytics
Decision Analytics Center of Excellence
2. Since its first aviation assignment for United Airlines in
1930, Booz Allen Hamilton, a leading strategy and technology
consulting firm, has completed more than 1,000 successful
engagements for clients in the airport, airline, aerospace,
and travel industries, as well as for air navigation service
providers and government and regulatory agencies worldwide.
In 2007, Booz Allen was named “Best Consulting Firm to the
Air Traffic Management Industry” by Air Traffic Management
magazine. Working with clients across the industry, we provide
deep functional knowledge spanning strategy, organization,
engineering, operations, technology, and analytics. Our
Decision Analytics Center of Excellence employs modeling
and simulation, decision science, operations research, and
quantitative analysis to deliver data-driven solutions that
improve performance, optimize flows, and help stakeholders
understand what and when events are likely to occur.
Booz Allen combines technical excellence with a core
understanding of the airline industry to solve problems in:
• Performance Benchmarking
• Network & Schedule Planning
• Fleet and Crew Optimization
• Pricing and Revenue Management
• Demand Modeling & Forecasting
• Customer Analytics and Marketing
• Big Data Mining and Data Architecture
• Technology Deployment and Evaluation
• Engineering and Maintenance Optimization
• Safety and Security Risk Modeling
• Legislative and Regulatory Analysis
• Competitive Analysis and Wargaming
Heat map of performance metrics across airline networkAirline OD Market Performance Visualizations
Visualizations specifically suited to represent big data allow Booz Allen to work with clients to quickly identify holistic, network-wide challenges and develop a complete set of solutions.
Source: Booz Allen Hamilton
Source: Booz Allen Hamilton
Valuable Insights Through Big Data Analysis:
A Commercial Airline Case Study
A commercial airline, faced with ever-increasing market
competition and challenges in profitability, engaged Booz Allen
to deploy advanced, globalized analytical tools to its private
big data warehouse. In the past, the client had analyzed
smaller datasets in-house, but because smaller datasets
are, by nature, filtered or diluted subsets of the big data,
the airline had not been able to extract the comprehensive
understanding it was seeking. The ultimate goal of Booz
Allen’s effort was to generate valuable insights from the big
data that would not be readily apparent from studying smaller,
localized datasets.
Our Approach
Booz Allen began with a thorough review of hardware and
software options to identify the best solutions for our client’s
particular needs. Working collaboratively with the client,
we defined the computing requirements necessary for our
team to access the client’s big data warehouse. Through
this interface, Booz Allen leveraged a suite of big data
analysis and visualization tools which, for this client, included
Revolution R, BayesiaLab, Tableau, and JMP. By combining
3. cutting-edge big data technology, advanced statistical and
probabilistic techniques, and our airline market expertise, we
were able to implement solutions both to explore the big data
warehouse and to visualize the powerful results therein.
Using the big data infrastructure implemented for the client,
Booz Allen was able to analyze large datasets containing more
than 3 years’ worth of passenger data—approximately 100
gigabytes. We generated hypotheses to test from the data,
including:
Airline Market Performance
• What are the client’s natural market types and their distinct
attributes?
• What is the client’s competitive market health?
• Where does the client capture fare premiums or fare
discounts relative to other carriers?
Booking Channel
Pax Count
Freq. Flyer
Freq. Flyer Level
Orig. AP
Orig. Cty
Orig. Region
Dest. AP
Dest. Cty
Dest. Region
Connection Pt. #1
Leg 1 Departure Time min
CT
Leg 2 Departure Time min
CT
Leg 1 Flight Time
Leg 2 Flight Time
Connection Time
Total Flight Time
OD Pax %
OD Revenue %
Orig. AP Pax %
Orig. AP Departure %
Orig. AP Seat %
Orig. Cty Pax %
Orig. Cty Departure %
Orig. Cty Seat %
Dest. AP Pax %
Dest. AP Departure %
Dest. AP Seat %
ppppp
Dest. Cty Pax %
Dest. Cty Departure %
Dest. Cty Seat %
Orig. Avg. HH Inc. 60mi
Orig. Pop. 60mi
Orig. Avg. HH Inc. 120mi
Orig. Pop. 120mi
Dest. Avg. HH Inc 60mi
Dest. Pop. 60mi
Dest. Avg. HH Iinc 120mi
Dest. Pop. 120mi
Expected Fare Value
Advance Booking Days
Booking Day
Flight Day
Connection Type
OD Distance
Bayesian Belief Network Representation of Passenger Behavior
Booz Allen employs Bayesian Belief Networks to identify relationships between factors
andconduct probabilistic analyses to conduct “what-if” analysis of nearly limitless scenarios.
Source: Booz Allen Hamilton
Airline market portfolio, 2006–11 Airline risk-return (Sharpe) ratio, 2006–11
Booz Allen analyzed the health of the client’s portfolio of Origin-Destination markets over a
decade, providing key measures of return on investment based on passenger traffic and revenue.
Source: Booz Allen Hamilton
Passenger Behavior
• What is the variability of booking curves by market type?
• What are the intrinsic attributes of markets with the
highest earn and highest burn rates?
• Can predictive modeling be developed for reservation
changes and no-show rates for individual passengers on
individual itineraries?
Consumer Choice
• What is the demand impact of increasing connection time?
• What is the effect of direct versus connecting itineraries
on passenger preference?
Our Results
The results from mining this data included inferences
about passenger booking behavior, airline performance,
and market characteristics. Although these insights often
seemed unintuitive, they could be acted on immediately by
the airline to improve its performance.
Case Study Summary
The big data analysis capabilities Booz Allen implemented
together with its airline partner are an industry first, and an
area of rapid growth across the travel and hospitality sector.
All of the big data capabilities we developed are software-
agnostic, so they can be readily tailored for specific needs
where client’s would derive value from mining historical data,
analyze simulation results, or develop predictive models.
Key Benefits of Big Data Analytics
Research suggests that even simple algorithms applied to
complete datasets are more useful than complex algorithms
applied to samples and extracts. Booz Allen is driving the
paradigm shift toward cloud-enabled big data analytics by
providing a cloud “insight-engine,” built on simple turnkey
solutions applied to full datasets. Our Big Data Analytics
capabilities provide a framework to collect and optimize large
datasets, baseline and improve performance, understand
how and why unique events occur, and simulate the impact of
changes. Potential benefits and applications include:
• Real-time optimization of flight schedules
• Optimized pricing based on greater fidelity of passenger
data
• More strategic marketing efforts based on targeted
analytical analysis
• Increased customer satisfaction with loyalty programs
driving tailored rewards